AI & Data Systems005
Retrieval, inference and data pipelines with evaluation attached — a model ships when its behaviour is measured, not when the demo goes well.
- Retrieval pipeline
- Evaluation harness
- Cost guardrails
- Data contracts
Whether you're a rising startup or an established name, we engage this service the same way we engage every other one — a diagnostic before a proposal, and a shipped result measured against what we said it would do.
Service Overview
We build retrieval, inference and data pipelines with evaluation attached from the start — a model ships when its behaviour is measured against real cases, not when the demo goes well in front of a stakeholder.
Cost guardrails, data contracts and a human review path for low-confidence outputs are part of the design, not an afterthought bolted on once a system is already in front of users.
Client Goals
Ship a system whose behaviour is backed by an evaluation harness with real cases, not one that only looks good in a live demo.
Know the per-query cost of the system before it's in front of users, with guardrails that stop a runaway bill before it happens.
Route low-confidence outputs to a person before they reach a customer, rather than trusting every response by default.
What Every Engagement Gets
Four commitments that hold the same way on every project, regardless of scope or budget.
Evaluation-First
An evaluation harness with real cases exists before the system is considered done, not after a complaint.
Data Contracts Enforced
Pipelines fail loudly on a schema change instead of silently feeding bad data downstream.
Cost Guardrails
Spend per query is measured and capped before the system meets real traffic.
Escalation to a Human
Low-confidence outputs are routed to a reviewer rather than shipped with false confidence.
Tech Stack
The same toolkit behind every service, chosen for the discipline it enables.
Cloud-native compute, declarative orchestration, and global edge routing engineered for zero-downtime scalability.
Type-safe, memory-efficient languages and low-latency runtimes selected for high concurrency, fast startup, and long-term maintainability.
Relational, document, and distributed event streaming datastores tuned for strict ACID guarantees, high throughput, and replication.
Production-grade vector indexing, evaluation harnesses, and cognitive retrieval pipelines backing intelligent enterprise systems.
Automated CI/CD pipelines, end-to-end testing harnesses, and real-time observability telemetry guarding production releases.
Questions on ai & data work.
An evaluation harness with real cases runs before the system is called done — not a demo that went well in front of a stakeholder. Behaviour is measured against labelled examples continuously, so quality is a number you can track, not a story you retell.
Most engagements start without a clean dataset. We help define what good looks like, sample and label a working set from your existing operation, and build the pipeline so evaluation improves as real usage accumulates.
Per-query cost is measured and capped before the system meets real traffic — routing, caching and model selection are tuned against a budget agreed at scoping, with alerts that catch a runaway pattern before it becomes an invoice.
Yes — pipelines are built against the warehouse and contracts you already run. Schema changes fail loudly instead of silently feeding bad data downstream, so the AI layer stays consistent with the rest of your platform.
Technical briefingNot a sales desk